Overview

Dataset statistics

Number of variables11
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.7 KiB
Average record size in memory89.3 B

Variable types

Numeric10
Categorical1

Warnings

Metropolitan Area has a high cardinality: 100 distinct values High cardinality
#ERROR! is highly correlated with Moderately Burdened Renter HouseholdsHigh correlation
Cost Burdened Renter Share (%) is highly correlated with Severely Burdened Renter Share (%) and 2 other fieldsHigh correlation
Severely Burdened Renter Share (%) is highly correlated with Cost Burdened Renter Share (%) and 2 other fieldsHigh correlation
Cost Burdened Owner Share (%) is highly correlated with Cost Burdened Renter Share (%) and 4 other fieldsHigh correlation
Severely Burdened Owner Share (%) is highly correlated with Cost Burdened Renter Share (%) and 5 other fieldsHigh correlation
Moderately Burdened Renter Households is highly correlated with #ERROR! and 1 other fieldsHigh correlation
Median Income of Renter Households is highly correlated with Median Income of Owner Households and 2 other fieldsHigh correlation
Median Income of Owner Households is highly correlated with Median Income of Renter Households and 2 other fieldsHigh correlation
Median Monthly Housing Cost of Renter Households is highly correlated with Cost Burdened Owner Share (%) and 4 other fieldsHigh correlation
Median Monthly Housing Cost of Owner Households is highly correlated with Cost Burdened Owner Share (%) and 4 other fieldsHigh correlation
#ERROR! is highly correlated with Moderately Burdened Renter Households and 1 other fieldsHigh correlation
Cost Burdened Renter Share (%) is highly correlated with Severely Burdened Renter Share (%) and 2 other fieldsHigh correlation
Severely Burdened Renter Share (%) is highly correlated with Cost Burdened Renter Share (%) and 2 other fieldsHigh correlation
Cost Burdened Owner Share (%) is highly correlated with Cost Burdened Renter Share (%) and 4 other fieldsHigh correlation
Severely Burdened Owner Share (%) is highly correlated with Cost Burdened Renter Share (%) and 3 other fieldsHigh correlation
Moderately Burdened Renter Households is highly correlated with #ERROR! and 4 other fieldsHigh correlation
Median Income of Renter Households is highly correlated with #ERROR! and 4 other fieldsHigh correlation
Median Income of Owner Households is highly correlated with Moderately Burdened Renter Households and 3 other fieldsHigh correlation
Median Monthly Housing Cost of Renter Households is highly correlated with Cost Burdened Owner Share (%) and 5 other fieldsHigh correlation
Median Monthly Housing Cost of Owner Households is highly correlated with Cost Burdened Owner Share (%) and 4 other fieldsHigh correlation
#ERROR! is highly correlated with Moderately Burdened Renter HouseholdsHigh correlation
Cost Burdened Renter Share (%) is highly correlated with Severely Burdened Renter Share (%) and 1 other fieldsHigh correlation
Severely Burdened Renter Share (%) is highly correlated with Cost Burdened Renter Share (%)High correlation
Cost Burdened Owner Share (%) is highly correlated with Cost Burdened Renter Share (%) and 2 other fieldsHigh correlation
Severely Burdened Owner Share (%) is highly correlated with Cost Burdened Owner Share (%)High correlation
Moderately Burdened Renter Households is highly correlated with #ERROR!High correlation
Median Income of Renter Households is highly correlated with Median Income of Owner Households and 2 other fieldsHigh correlation
Median Income of Owner Households is highly correlated with Median Income of Renter Households and 2 other fieldsHigh correlation
Median Monthly Housing Cost of Renter Households is highly correlated with Cost Burdened Owner Share (%) and 3 other fieldsHigh correlation
Median Monthly Housing Cost of Owner Households is highly correlated with Median Income of Renter Households and 2 other fieldsHigh correlation
Median Monthly Housing Cost of Owner Households is highly correlated with Median Income of Renter Households and 6 other fieldsHigh correlation
#ERROR! is highly correlated with Moderately Burdened Renter Households and 1 other fieldsHigh correlation
Median Income of Renter Households is highly correlated with Median Monthly Housing Cost of Owner Households and 5 other fieldsHigh correlation
Severely Burdened Owner Share (%) is highly correlated with Median Monthly Housing Cost of Owner Households and 7 other fieldsHigh correlation
Median Income of Owner Households is highly correlated with Median Monthly Housing Cost of Owner Households and 5 other fieldsHigh correlation
Moderately Burdened Renter Households is highly correlated with Median Monthly Housing Cost of Owner Households and 4 other fieldsHigh correlation
Median Monthly Housing Cost of Renter Households is highly correlated with Median Monthly Housing Cost of Owner Households and 7 other fieldsHigh correlation
Metropolitan Area is highly correlated with Median Monthly Housing Cost of Owner Households and 9 other fieldsHigh correlation
Cost Burdened Owner Share (%) is highly correlated with Median Monthly Housing Cost of Owner Households and 6 other fieldsHigh correlation
Cost Burdened Renter Share (%) is highly correlated with Severely Burdened Owner Share (%) and 5 other fieldsHigh correlation
Severely Burdened Renter Share (%) is highly correlated with Severely Burdened Owner Share (%) and 2 other fieldsHigh correlation
#ERROR! is uniformly distributed Uniform
Metropolitan Area is uniformly distributed Uniform
#ERROR! has unique values Unique
Metropolitan Area has unique values Unique

Reproduction

Analysis started2021-10-07 19:01:18.991950
Analysis finished2021-10-07 19:01:46.433331
Duration27.44 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

#ERROR!
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2021-10-08T00:31:46.625979image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.01149198
Coefficient of variation (CV)0.5744849896
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.6666667
MonotonicityStrictly increasing
2021-10-08T00:31:46.830474image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
1.0%
641
 
1.0%
741
 
1.0%
731
 
1.0%
721
 
1.0%
711
 
1.0%
701
 
1.0%
691
 
1.0%
681
 
1.0%
671
 
1.0%
Other values (90)90
90.0%
ValueCountFrequency (%)
11
1.0%
21
1.0%
31
1.0%
41
1.0%
51
1.0%
61
1.0%
71
1.0%
81
1.0%
91
1.0%
101
1.0%
ValueCountFrequency (%)
1001
1.0%
991
1.0%
981
1.0%
971
1.0%
961
1.0%
951
1.0%
941
1.0%
931
1.0%
921
1.0%
911
1.0%

Metropolitan Area
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
New YorkNewarkJersey City NYNJPA
 
1
Knoxville TN
 
1
GreensboroHigh Point NC
 
1
North PortSarasotaBradenton FL
 
1
Dayton OH
 
1
Other values (95)
95 

Length

Max length40
Median length23
Mean length22.43
Min length8

Characters and Unicode

Total characters2243
Distinct characters51
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowNew YorkNewarkJersey City NYNJPA
2nd rowLos AngelesLong BeachAnaheim CA
3rd rowChicagoNapervilleElgin ILINWI
4th rowDallasFort WorthArlington TX
5th rowHoustonThe WoodlandsSugar Land TX

Common Values

ValueCountFrequency (%)
New YorkNewarkJersey City NYNJPA1
 
1.0%
Knoxville TN1
 
1.0%
GreensboroHigh Point NC1
 
1.0%
North PortSarasotaBradenton FL1
 
1.0%
Dayton OH1
 
1.0%
Columbia SC1
 
1.0%
Baton Rouge LA1
 
1.0%
AllentownBethlehemEaston PANJ1
 
1.0%
El Paso TX1
 
1.0%
McAllenEdinburgMission TX1
 
1.0%
Other values (90)90
90.0%

Length

2021-10-08T00:31:47.395305image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca10
 
3.8%
fl9
 
3.4%
tx6
 
2.3%
city5
 
1.9%
oh5
 
1.9%
ny4
 
1.5%
san4
 
1.5%
nc4
 
1.5%
new3
 
1.1%
pa3
 
1.1%
Other values (190)209
79.8%

Most occurring characters

ValueCountFrequency (%)
a175
 
7.8%
162
 
7.2%
e160
 
7.1%
o151
 
6.7%
n141
 
6.3%
r127
 
5.7%
l112
 
5.0%
i108
 
4.8%
t93
 
4.1%
s90
 
4.0%
Other values (41)924
41.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1549
69.1%
Uppercase Letter532
 
23.7%
Space Separator162
 
7.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C60
 
11.3%
A58
 
10.9%
N42
 
7.9%
S38
 
7.1%
M31
 
5.8%
L27
 
5.1%
W26
 
4.9%
T26
 
4.9%
O25
 
4.7%
H24
 
4.5%
Other values (15)175
32.9%
Lowercase Letter
ValueCountFrequency (%)
a175
11.3%
e160
10.3%
o151
9.7%
n141
9.1%
r127
 
8.2%
l112
 
7.2%
i108
 
7.0%
t93
 
6.0%
s90
 
5.8%
d59
 
3.8%
Other values (15)333
21.5%
Space Separator
ValueCountFrequency (%)
162
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2081
92.8%
Common162
 
7.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a175
 
8.4%
e160
 
7.7%
o151
 
7.3%
n141
 
6.8%
r127
 
6.1%
l112
 
5.4%
i108
 
5.2%
t93
 
4.5%
s90
 
4.3%
C60
 
2.9%
Other values (40)864
41.5%
Common
ValueCountFrequency (%)
162
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2243
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a175
 
7.8%
162
 
7.2%
e160
 
7.1%
o151
 
6.7%
n141
 
6.3%
r127
 
5.7%
l112
 
5.0%
i108
 
4.8%
t93
 
4.1%
s90
 
4.0%
Other values (41)924
41.2%

Cost Burdened Renter Share (%)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct74
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.198
Minimum39.1
Maximum61.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2021-10-08T00:31:47.564411image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum39.1
5-th percentile42.395
Q145.4
median47.6
Q350.35
95-th percentile55.525
Maximum61.5
Range22.4
Interquartile range (IQR)4.95

Descriptive statistics

Standard deviation4.285186605
Coefficient of variation (CV)0.08890797555
Kurtosis0.007744023969
Mean48.198
Median Absolute Deviation (MAD)2.6
Skewness0.5508862993
Sum4819.8
Variance18.36282424
MonotonicityNot monotonic
2021-10-08T00:31:47.730126image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45.94
 
4.0%
43.93
 
3.0%
46.33
 
3.0%
47.83
 
3.0%
46.43
 
3.0%
54.63
 
3.0%
47.42
 
2.0%
50.52
 
2.0%
50.22
 
2.0%
49.82
 
2.0%
Other values (64)73
73.0%
ValueCountFrequency (%)
39.11
1.0%
40.61
1.0%
40.91
1.0%
42.11
1.0%
42.31
1.0%
42.41
1.0%
42.91
1.0%
431
1.0%
43.11
1.0%
43.32
2.0%
ValueCountFrequency (%)
61.51
 
1.0%
57.11
 
1.0%
571
 
1.0%
56.71
 
1.0%
561
 
1.0%
55.51
 
1.0%
55.11
 
1.0%
54.81
 
1.0%
54.71
 
1.0%
54.63
3.0%

Severely Burdened Renter Share (%)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct68
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.273
Minimum18.4
Maximum35.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2021-10-08T00:31:47.934320image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum18.4
5-th percentile20.195
Q123
median24.85
Q327.7
95-th percentile30.615
Maximum35.4
Range17
Interquartile range (IQR)4.7

Descriptive statistics

Standard deviation3.259790382
Coefficient of variation (CV)0.1289831196
Kurtosis-0.1270987521
Mean25.273
Median Absolute Deviation (MAD)2.35
Skewness0.3039180672
Sum2527.3
Variance10.62623333
MonotonicityNot monotonic
2021-10-08T00:31:48.111362image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.45
 
5.0%
24.84
 
4.0%
22.73
 
3.0%
25.83
 
3.0%
24.33
 
3.0%
233
 
3.0%
28.92
 
2.0%
27.72
 
2.0%
27.22
 
2.0%
30.92
 
2.0%
Other values (58)71
71.0%
ValueCountFrequency (%)
18.41
1.0%
18.61
1.0%
19.71
1.0%
19.81
1.0%
20.11
1.0%
20.22
2.0%
20.71
1.0%
21.52
2.0%
21.62
2.0%
21.71
1.0%
ValueCountFrequency (%)
35.41
1.0%
31.81
1.0%
311
1.0%
30.92
2.0%
30.61
1.0%
30.51
1.0%
30.31
1.0%
30.21
1.0%
29.91
1.0%
29.31
1.0%

Cost Burdened Owner Share (%)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct73
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.651
Minimum17.5
Maximum36.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2021-10-08T00:31:48.295629image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum17.5
5-th percentile18
Q120.325
median22.85
Q326.4
95-th percentile33.44
Maximum36.1
Range18.6
Interquartile range (IQR)6.075

Descriptive statistics

Standard deviation4.563568845
Coefficient of variation (CV)0.1929545831
Kurtosis0.3346224643
Mean23.651
Median Absolute Deviation (MAD)2.9
Skewness0.901561653
Sum2365.1
Variance20.82616061
MonotonicityNot monotonic
2021-10-08T00:31:48.466727image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
184
 
4.0%
21.73
 
3.0%
20.43
 
3.0%
18.72
 
2.0%
20.62
 
2.0%
23.62
 
2.0%
18.92
 
2.0%
19.82
 
2.0%
23.82
 
2.0%
26.42
 
2.0%
Other values (63)76
76.0%
ValueCountFrequency (%)
17.51
 
1.0%
17.72
2.0%
17.91
 
1.0%
184
4.0%
18.21
 
1.0%
18.32
2.0%
18.61
 
1.0%
18.72
2.0%
18.92
2.0%
19.11
 
1.0%
ValueCountFrequency (%)
36.11
1.0%
35.71
1.0%
35.21
1.0%
34.71
1.0%
34.21
1.0%
33.41
1.0%
32.71
1.0%
31.41
1.0%
31.11
1.0%
30.41
1.0%

Severely Burdened Owner Share (%)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.909
Minimum5
Maximum17.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2021-10-08T00:31:48.701220image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6.895
Q18.075
median9.5
Q311.225
95-th percentile14.62
Maximum17.1
Range12.1
Interquartile range (IQR)3.15

Descriptive statistics

Standard deviation2.435100852
Coefficient of variation (CV)0.2457463773
Kurtosis1.077371467
Mean9.909
Median Absolute Deviation (MAD)1.5
Skewness0.9261017057
Sum990.9
Variance5.929716162
MonotonicityNot monotonic
2021-10-08T00:31:48.905920image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.65
 
5.0%
9.54
 
4.0%
9.34
 
4.0%
8.44
 
4.0%
10.14
 
4.0%
7.64
 
4.0%
7.53
 
3.0%
7.23
 
3.0%
83
 
3.0%
11.53
 
3.0%
Other values (46)63
63.0%
ValueCountFrequency (%)
51
 
1.0%
6.21
 
1.0%
6.31
 
1.0%
6.51
 
1.0%
6.81
 
1.0%
6.91
 
1.0%
71
 
1.0%
7.12
2.0%
7.23
3.0%
7.53
3.0%
ValueCountFrequency (%)
17.11
1.0%
171
1.0%
16.81
1.0%
16.71
1.0%
151
1.0%
14.61
1.0%
13.71
1.0%
13.51
1.0%
13.31
1.0%
13.22
2.0%

Moderately Burdened Renter Households
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70948
Minimum10600
Maximum792800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2021-10-08T00:31:49.093931image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum10600
5-th percentile14755
Q121875
median32650
Q375450
95-th percentile212355
Maximum792800
Range782200
Interquartile range (IQR)53575

Descriptive statistics

Standard deviation106107.9758
Coefficient of variation (CV)1.495573882
Kurtosis26.27999295
Mean70948
Median Absolute Deviation (MAD)14850
Skewness4.610459489
Sum7094800
Variance1.125890252 × 1010
MonotonicityNot monotonic
2021-10-08T00:31:49.593134image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
261002
 
2.0%
975002
 
2.0%
247002
 
2.0%
270002
 
2.0%
253002
 
2.0%
184002
 
2.0%
321002
 
2.0%
148001
 
1.0%
106001
 
1.0%
192001
 
1.0%
Other values (83)83
83.0%
ValueCountFrequency (%)
106001
1.0%
129001
1.0%
130001
1.0%
134001
1.0%
139001
1.0%
148001
1.0%
155001
1.0%
168001
1.0%
170001
1.0%
175001
1.0%
ValueCountFrequency (%)
7928001
1.0%
5865001
1.0%
2849001
1.0%
2348001
1.0%
2248001
1.0%
2117001
1.0%
1915001
1.0%
1751001
1.0%
1747001
1.0%
1656001
1.0%

Median Income of Renter Households
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct59
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35785.6
Minimum24400
Maximum76500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2021-10-08T00:31:49.905125image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum24400
5-th percentile26890
Q130000
median35000
Q338375
95-th percentile50085
Maximum76500
Range52100
Interquartile range (IQR)8375

Descriptive statistics

Standard deviation8207.236437
Coefficient of variation (CV)0.2293446648
Kurtosis6.48206667
Mean35785.6
Median Absolute Deviation (MAD)4800
Skewness2.039104651
Sum3578560
Variance67358729.94
MonotonicityNot monotonic
2021-10-08T00:31:50.144595image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3000011
 
11.0%
360008
 
8.0%
350007
 
7.0%
280003
 
3.0%
290003
 
3.0%
500002
 
2.0%
340002
 
2.0%
440002
 
2.0%
325002
 
2.0%
310002
 
2.0%
Other values (49)58
58.0%
ValueCountFrequency (%)
244001
 
1.0%
250001
 
1.0%
254001
 
1.0%
260001
 
1.0%
267001
 
1.0%
269001
 
1.0%
270001
 
1.0%
272201
 
1.0%
276001
 
1.0%
280003
3.0%
ValueCountFrequency (%)
765001
1.0%
610001
1.0%
600001
1.0%
580001
1.0%
517001
1.0%
500002
2.0%
450002
2.0%
446001
1.0%
440002
2.0%
435002
2.0%

Median Income of Owner Households
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct78
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74457.4
Minimum41000
Maximum130200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2021-10-08T00:31:50.662138image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum41000
5-th percentile57825
Q164980
median71000
Q382210
95-th percentile103053.5
Maximum130200
Range89200
Interquartile range (IQR)17230

Descriptive statistics

Standard deviation15173.72015
Coefficient of variation (CV)0.2037906259
Kurtosis1.893049501
Mean74457.4
Median Absolute Deviation (MAD)8525
Skewness1.113562031
Sum7445740
Variance230241783.1
MonotonicityNot monotonic
2021-10-08T00:31:50.936065image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
650004
 
4.0%
600004
 
4.0%
680003
 
3.0%
840003
 
3.0%
1000002
 
2.0%
666002
 
2.0%
746002
 
2.0%
610002
 
2.0%
678002
 
2.0%
740002
 
2.0%
Other values (68)74
74.0%
ValueCountFrequency (%)
410001
 
1.0%
490101
 
1.0%
515001
 
1.0%
540001
 
1.0%
545001
 
1.0%
580002
2.0%
594801
 
1.0%
596001
 
1.0%
599001
 
1.0%
600004
4.0%
ValueCountFrequency (%)
1302001
1.0%
1186001
1.0%
1170501
1.0%
1100001
1.0%
1040701
1.0%
1030001
1.0%
1000002
2.0%
964001
1.0%
947001
1.0%
920001
1.0%

Median Monthly Housing Cost of Renter Households
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct70
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean964.67
Minimum640
Maximum1880
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2021-10-08T00:31:51.179141image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum640
5-th percentile730
Q1820
median912
Q31030
95-th percentile1445.5
Maximum1880
Range1240
Interquartile range (IQR)210

Descriptive statistics

Standard deviation224.9450687
Coefficient of variation (CV)0.2331834396
Kurtosis3.254972105
Mean964.67
Median Absolute Deviation (MAD)106.5
Skewness1.667324062
Sum96467
Variance50600.28394
MonotonicityNot monotonic
2021-10-08T00:31:51.367135image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8205
 
5.0%
10303
 
3.0%
9903
 
3.0%
9003
 
3.0%
10003
 
3.0%
7503
 
3.0%
9603
 
3.0%
9103
 
3.0%
8002
 
2.0%
8902
 
2.0%
Other values (60)70
70.0%
ValueCountFrequency (%)
6401
 
1.0%
7002
2.0%
7111
 
1.0%
7302
2.0%
7371
 
1.0%
7402
2.0%
7503
3.0%
7601
 
1.0%
7651
 
1.0%
7671
 
1.0%
ValueCountFrequency (%)
18801
1.0%
16141
1.0%
16001
1.0%
15601
1.0%
15501
1.0%
14401
1.0%
14301
1.0%
13541
1.0%
13002
2.0%
12681
1.0%

Median Monthly Housing Cost of Owner Households
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1175.63
Minimum558
Maximum2311
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2021-10-08T00:31:51.628575image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum558
5-th percentile789.75
Q1916
median1093
Q31327.75
95-th percentile1937.35
Maximum2311
Range1753
Interquartile range (IQR)411.75

Descriptive statistics

Standard deviation356.1963667
Coefficient of variation (CV)0.3029833933
Kurtosis1.314285143
Mean1175.63
Median Absolute Deviation (MAD)190.5
Skewness1.238784061
Sum117563
Variance126875.8516
MonotonicityNot monotonic
2021-10-08T00:31:51.982795image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10253
 
3.0%
10003
 
3.0%
12522
 
2.0%
12302
 
2.0%
20201
 
1.0%
5581
 
1.0%
8831
 
1.0%
8661
 
1.0%
9421
 
1.0%
9041
 
1.0%
Other values (84)84
84.0%
ValueCountFrequency (%)
5581
1.0%
6781
1.0%
7481
1.0%
7631
1.0%
7851
1.0%
7901
1.0%
8151
1.0%
8171
1.0%
8281
1.0%
8361
1.0%
ValueCountFrequency (%)
23111
1.0%
21971
1.0%
21781
1.0%
20211
1.0%
20201
1.0%
19331
1.0%
18831
1.0%
18441
1.0%
18321
1.0%
18201
1.0%

Interactions

2021-10-08T00:31:24.083910image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:24.218863image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:24.326928image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:24.514762image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:24.886727image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:25.093108image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:25.228281image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:25.344796image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:25.452160image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:25.567224image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:25.676754image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:25.848911image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:26.083665image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:26.210206image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:26.325509image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:26.437073image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:26.554137image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:26.688734image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:27.060977image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:27.212573image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:27.357762image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:27.478724image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:27.870162image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:28.152838image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:28.345649image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:28.611247image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:28.912260image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:29.174038image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:29.363186image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:29.503057image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:29.898400image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:30.221159image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:30.407580image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:30.637779image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:30.876395image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:31.174090image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:31.605337image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:31.732522image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:31.873230image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:32.238209image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:32.454217image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:32.597972image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:32.865725image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:33.012817image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:33.188132image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:33.363101image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:33.553623image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:33.993485image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:34.137140image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:34.365115image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:34.539557image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:34.729574image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:35.123731image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:35.336559image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:35.510928image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:35.713257image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:36.270797image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:36.474949image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:36.622514image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:36.788052image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:37.139461image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:37.342886image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:37.542651image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:37.688344image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:37.910287image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:38.043021image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:38.389879image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:38.563162image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:38.761141image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:38.968747image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:39.185054image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:39.463544image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:39.620680image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:39.754790image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:40.058131image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:40.199179image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:40.395900image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:40.556528image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:40.708006image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:40.834213image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:41.138772image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:41.382637image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:41.540039image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:41.961007image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:42.401710image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:42.641842image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:42.843421image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:43.224429image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:43.442576image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:43.669011image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:43.866001image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:44.010615image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:44.320632image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:44.592584image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:44.753670image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:44.893551image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:45.062212image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:45.253822image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:45.501268image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-08T00:31:45.674654image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-10-08T00:31:52.146518image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-08T00:31:52.411973image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-08T00:31:53.010687image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-08T00:31:53.413319image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-08T00:31:45.986786image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-08T00:31:46.287568image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

#ERROR!Metropolitan AreaCost Burdened Renter Share (%)Severely Burdened Renter Share (%)Cost Burdened Owner Share (%)Severely Burdened Owner Share (%)Moderately Burdened Renter HouseholdsMedian Income of Renter HouseholdsMedian Income of Owner HouseholdsMedian Monthly Housing Cost of Renter HouseholdsMedian Monthly Housing Cost of Owner Households
01New YorkNewarkJersey City NYNJPA52.529.936.117.17928004350010000013002020
12Los AngelesLong BeachAnaheim CA57.131.035.716.7586500440009195013541883
23ChicagoNapervilleElgin ILINWI50.327.927.812.1284900370008300010101415
34DallasFort WorthArlington TX45.922.722.38.623480040100810009901252
45HoustonThe WoodlandsSugar Land TX46.724.220.58.821170039600840009801204
56WashingtonArlingtonAlexandria DCVAMDWV45.922.623.99.21915006000011860015501933
67PhiladelphiaCamdenWilmington PANJDEMD51.028.628.012.1162900375008400010601404
78MiamiFort LauderdaleWest Palm Beach FL61.535.434.717.0224800350006450012001220
89AtlantaSandy SpringsRoswell GA47.825.323.010.1174700390007560010101167
910BostonCambridgeNewton MANH48.824.828.412.01656004500010407013001844

Last rows

#ERROR!Metropolitan AreaCost Burdened Renter Share (%)Severely Burdened Renter Share (%)Cost Burdened Owner Share (%)Severely Burdened Owner Share (%)Moderately Burdened Renter HouseholdsMedian Income of Renter HouseholdsMedian Income of Owner HouseholdsMedian Monthly Housing Cost of Renter HouseholdsMedian Monthly Housing Cost of Owner Households
9091Toledo OH45.524.918.98.1187002700065000700887
9192AugustaRichmond County GASC50.230.321.09.0139002760060000808873
9293ProvoOrem UT39.118.620.57.61060043000790509601260
9394Jackson MS49.428.920.710.7148002600061000830848
9495Palm BayMelbourneTitusville FL49.822.724.29.5182003600058000970836
9596HarrisburgCarlisle PA47.422.920.46.51810035600710008851088
9697ScrantonWilkesBarreHazleton PA45.727.723.811.2130002722060000740815
9798DurhamChapel Hill NC43.023.622.89.71750035600785009301130
9899YoungstownWarrenBoardman OHPA46.321.718.77.2170002440054000640678
99100SpokaneSpokane Valley WA50.024.826.410.92080029000631607701004